2026-04-28T17:12:34Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/74012023-03-27T06:55:36Zcom_10259_3830com_10259_5086com_10259_2604col_10259_3832
Santos Martín, José Ignacio
526
600
0000-0002-6653-043X
Martín, Óscar
b8e82c82-cd88-4bf9-880d-f98b7f75ec71
600
Ahedo García, Virginia
10
600
0000-0002-9812-388X
Tiedra, Pilar de
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600
Galán Ordax, José Manuel
188
600
0000-0003-3360-7602
2023-02-06T12:39:01Z
2023-02-06T12:39:01Z
2022-11
0268-3768
http://hdl.handle.net/10259/7401
10.1007/s00170-022-10444-4
1433-3015
Resistance spot welding (RSW) is one of the most relevant industrial processes in diferent sectors. Key issues in RSW are
process control and ex-ante and ex-post evaluation of the quality level of RSW joints. Multiple-input–single-output methods
are commonly used to create predictive models of the process from the welding parameters. However, until now, the choice
of a particular model has typically involved a tradeof between accuracy and interpretability. In this work, such dichotomy
is overcome by using the explainable boosting machine algorithm, which obtains accuracy levels in both classifcation and
prediction of the welded joint tensile shear load bearing capacity statistically as good or even better than the best algorithms
in the literature, while maintaining high levels of interpretability. These characteristics allow (i) a simple diagnosis of the
overall behavior of the process, and, for each individual prediction, (ii) the attribution to each of the control variables—and/
or to their potential interactions—of the result obtained. These distinctive characteristics have important implications for
the optimization and control of welding processes, establishing the explainable boosting machine as one of the reference
algorithms for their modeling.
Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. The authors acknowledge fnancial support from the Spanish Ministry of Science, Innovation and Universities (Excellence Network RED2018‐102518‐T), the Spanish State Research Agency (PID2020-118906 GB-I00/AEI/https://doi.org/10. 13039/501100011033), and the Fundación Bancaria Caixa D. Estalvis I Pensions de Barcelona, La Caixa (2020/00062/001). In addition, we acknowledge support from the Santander Supercomputación group (University of Cantabria) that provided access to the Altamira Supercomputer—located at the Institute of Physics of Cantabria (IFCACSIC) and member of the Spanish Supercomputing Network—to perform the diferent simulations/analyses.
application/pdf
eng
Springer Nature
The International Journal of Advanced Manufacturing Technology. 2022, V. 123, n. 11-12, p. 4077-4092
https://doi.org/10.1007/s00170-022-10444-4
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RED2018‐102518‐T/ES/SISTEMAS COMPLEJOS SOCIOTECNOLOGICOS/
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-118906GB-I00/ES/INTERACCIONES DINAMICAS DISTRIBUIDAS: PROTOCOLOS BEST EXPERIENCED PAYOFF Y SEPARACION ENDOGENA/
info:eu-repo/grantAgreement/Fundación Bancaria Caixa d'Estalvis i Pensions de Barcelona//2020%2F00062%2F001/
Atribución 4.0 Internacional
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
Explainable boosting machine
Pattern recognition
Quality assessment
Resistance spot welding
AISI 304 austenitic stainless steel
Tensile shear load bearing capacity
Ingeniería
Engineering
Glass-box modeling for quality assessment of resistance spot welding joints in industrial applications
info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
The International Journal of Advanced Manufacturing Technology
123
11-12
4077
4092
THUMBNAIL
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oai:riubu.ubu.es:10259/7401
2023-03-27 08:55:36.449
Repositorio Institucional de la Universidad de Burgos
bubrep@ubu.es
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